题  目：A Continuous Optimization Model for Clustering

主讲人：张在坤（博士） 香港理工大学应用数学系

摘  要：We study the problem of clustering a set of objects into groups according to a certain measure of similarity among the objects. This is one of the basic problems in data processing with various applications ranging from computer science to social analysis. We propose a new continuous model for this problem, the idea being to seek a balance between maximize-ing the number of clusters and minimizing the similarity among the objects from distinct clusters. Adopting the spectral clustering methodology, our model quantifies  the number of clusters via the rank of a graph Laplacian, and then  relaxes rank minimization to trace minimization with orthogonal constraints. We analyze the properties of our model, propose a block coordinate descent algorithm for it, and establish the global convergence of the algorithm. We then demonstrate our model and algorithm by several numerical examples.

时  间：2018年9月29日（星期六） 下午14:30-15:30
地  点：数学科学学院424室

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数学科学学院     
2018年9月21日